Accelerate Ray in Your Environment
Boost processing performance for faster task completion.
Shrink clusters and cut resource costs.
Boost processing performance for faster job completion.
Ray vs. Flarion-Powered Ray
Core Capabilities
Upgrade Ray's execution engine for unmatched speed and efficiency combining the best of both.
Automatic fallback to Ray API for stability when native optimization isn’t available.
Works across AWS, Azure, Google Cloud, and on-prem environments.
Agentless design protects data with minimal permissions.
Scales with cluster growth, enhancing performance.
How Flarion’s Ray Accelerator Works
Standard Ray distributes tasks across machines but is constrained by the inefficiencies of Ray Core, leading to:
- Higher Resource Usage
- Slower Processing
- Limited Optimization
Flarion-powered Ray replaces Ray Core with Flarion's Polars and Arrow-based engine for acceleration of operators and expressions like filter, groupBy, and join - no code changes needed.
Ray Core provides low level data processing but its implementation can limit performance on compute-intensive workloads.
Flarion enhances Ray by integrating a Polars-powered execution engine, compiling tasks into optimized Rust code to accelerate CPU-bound operations—no code changes, no disruptions.
Flarion Integration with Ray Architecture
Flarion Accelerator integrates with Ray by optimizing task execution using high-performance native engine while Ray continues to manage task scheduling and distributed execution.
Integration Across
All Platforms
Deployed using Ray clusters on EC2 instances.
Configured with Ray clusters on GCP Compute Engine.
Integrated with Azure Virtual Machines running Ray clusters.
Deploy with Helm charts or Ray operator; Kubernetes handles scaling while Flarion optimizes in real-time.
Install on Ray nodes using tools like Ansible or Chef, optimizing task execution.
Plug & Play in Seconds
import ray
ray.init()
# Define a remote actor class
@ray.remote
class FlarionEngine
2x Faster Processing And 35% Cost Savings
Minimal Effort
No code changes or tuning needed for immediate performance boosts.
Stability
Smaller, more stable clusters reduce node failures for resilient operations.
Resource Usage
Lower infrastructure demands, enabling efficient data processing.
